Online supplemental material 1

2

3

Effect of snowpack management on grassland 4

biodiversity and soil properties at a ski-resort in 5

the Mediterranean basin (central Italy) 6

Authors: M. ALLEGREZZA1*, S. COCCO1, S. PESARESI1, F. COURCHESNE2 & G. CORTI1 7

Affiliations: 1Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic 8

University, Italy and 2Département de Géographie, Université de Montréal, Montréal, Québec, 9

Canada 10

11

*Correspondence: M. Allegrezza, Department of Agricultural, Food and Environmental Sciences, 12

Marche Polytechnic University, Ancona 60100, Italy. Tel.: (+39) 071 2204951. Fax: (+39) 071 13

2204953. Email: m.allegrezza@univpm.it 14

Attachment 1. Map of Italy indicating the ski-resorts on the Apennines mountains. White dots refer 15

to resorts below the altitude of 2000 m, gray dots refer to resorts over 2000 m and the black box 16

localizes the studied resort (Sassotetto ski-resort, Sarnano), at altitudes from 1360 to 1610 m. 17

18

19

20

Attachment 2. The study area at the Sassotetto ski-resort. Black dots indicate the plots for 21

vegetational observations. UG, undisturbed grassland area; NS, ski-runs with natural snow; AS, ski-22

runs with amassed and artificial snow. 23

24

25

26

Attachment 3. Details on Materials and methods 27

Thermistors’ details 28

YSI external thermistors have a thermometric drift for 0°C of less than 0.01°C in 100 months while 29

dataloggers have an accuracy of 0.1°C in the range +20/-20°C, and of 0.2°C in the range +40/-30

40°C, with a resolution of 0.1°C. 31

Soil analysis 32

On the refrigerated samples, the following analyses were run within three weeks from the sampling. 33

Total dissolved carbon (TDC) and total dissolved nitrogen (TDN) were extracted by 0.5 M 34

K2SO4 solution (1:4 soil:extractant ratio). On the extracts, TDC and dissolved inorganic carbon 35

(DIC) were determined on a Shimadzu TOC-V CPN analyser (Shimadzu Corp., Tokyo, Japan) with 36

IR detection following thermal oxidation. The dissolved organic carbon (DOC) content was 37

estimated as the difference between TDC and DIC. The TDN was determined after alkaline 38

persulfate digestion (Koroleff 1983; Qualls 1989) as NO3 with a continuous flow auto analyser. The 39

extracts were analysed for dissolved inorganic nitrogen (DIN) (NO3-N and NH4-N) using a 40

continuous flow auto analyser (Chelan System 4) and the dissolved organic nitrogen (DON) was 41

calculated as the difference between TDN and DIN. Soil microbial biomass carbon (MB-C) and 42

microbial biomass nitrogen (MB-N) were determined by fumigation–extraction method (Brookes et 43

al. 1985; Vance et al. 1987). For each sample, about 15 g of fine earth were fumigated with ethanol-44

free chloroform for 24 h at 25°C in an evacuated extractor, while another 15 g of fine earth were not 45

fumigated (control). Fumigated and non-fumigated samples were extracted with 60 ml of a 0.5 M 46

K2SO4 solution and shaken for 1 h. The extracts were filtered using Whatman No. 42 filter paper 47

and stored at -5°C prior to analysis. Within one week from the extraction, organic C and N were 48

measured using a Multi N/C 3000 analyser (Elementar Analysensysteme GmbH). 49

On the air-dried samples, the following analyses were run. The pH was determined 50

potentiometrically in water (1:2.5 solid:liquid ratio). The total organic C (TOC) content was 51

obtained with a dry combustion analyser (EA-1110, Carlo Erba Instruments, Milan, Italy), after 52

acidification of the aliquot to be analysed. The humic C (HC) content was estimated by the 53

Walkley-Black method without application of heat (Allison 1965). Available P was determined 54

according to Olsen et al. (1954). The mineralogical assemblage of the whole samples was assessed 55

on powdered specimens by x-ray diffraction with a Philips PW 1830 diffractometer, using the Fe-56

filtered Co K1 radiation (35 kV and 25 mA). 57

Vegetation analysis 58

Vegetation data. The chorological types were grouped as reported in Supplemental 59

attachment 6. In the case of biological forms (attachment 6), caespitose, reptant, scapose, rosette, 60

and biannual sub-forms were used only for the hemicryptophytes (the most abundant). For the 61

Ellenberg indicators (Ellenberg et al. 1992), we used the indexes re-formulated for the 62

Mediterranean conditions (Pignatti et al. 2005): L = light, T = temperatures, C = continentality; U = 63

soil moisture, R = soil reaction, N = availability of soil nutrients. 64

Topographic data. The topographic position index (TPI) (Guisan et al. 1999) represents the 65

relative topographic position of a locality and was obtained by difference between the quote of each 66

plot and the mean quote of the DEM cells considered within a radius of 100, 200, 300 and 400 m. 67

Positive TPI indicates crests or slope segments at the highest altitudes, while negative values refer 68

to valleys or slope segments at the lowest altitudes; a TPI close to zero indicates flat land surfaces 69

or slopes with constant inclination. The topographic wetness index (TWI) was calculated by the 70

homonym module in SAGA GIS (Conrad et al. 2015). The TWI provides a relative measure of the 71

potential moisture status of a particular land surface. The annual solar radiation (SS) was estimated 72

by the Point Solar Radiation tool (ESRI ArcGis 9.3), which calculates total solar radiation at a 73

locality by considering altitude, exposure, slope and shadowing. To establish whether differences in 74

floristic and main functional traits were related to topography or/and land management, the 75

following variables were used: management of ski-runs (levels: UG, NS, AS), elevation (QUO), 76

aspect (ASP), slope (SLO), TPI with different radius (TPI1, TPI2, TPI3, TPI4, with radius of 100, 77

200, 300 and 400 m, respectively), TWI, and SS. 78

Statistical analysis 79

Soil data. For the soil analyses, all measurements were duplicated (two aliquots for each 80

horizon of a given profile), and the two values per horizon were averaged. These averages were 81

used to calculate the mean for a given horizon (n=2 profiles), and the standard deviation was 82

calculated for n=2. The data were tested for the normality of the distribution and the homogeneity 83

of the variances by the Shapiro-Wilk and Levene tests, respectively. Because of the not-gaussian 84

distribution of the dataset, box plot diagrams were used to illustrate differences between soils for 85

each parameter. For every parameter, the soil weighed mean was calculated by taking into 86

consideration the thickness of each horizon, and the data were standardized by subtracting the mean 87

and dividing by the standard deviation. In the plots, the bottom and top of the box are the first and 88

third quartiles, the upper and lower whiskers indicate the minimum and maximum values, and the 89

dot sign within each box plot indicates the average (n=2). The lack of overlapping among box plots 90

indicates a statistically significant difference (Wild et al. 2011; Krzywinski & Altman 2014). A 91

standardized principal component analysis (PCA) with weighted mean soil parameters was 92

performed to assess the differences between soils. The statistical analyses were run using R 93

software (R Core Team 2012). 94

Ski-run effects on vegetation richness and composition. The redundancy data analysis 95

(RDA) model performed to assess the extent of grassland variation was tested for significance by 96

using 999 random permutations. The community-weighted mean trait value (CWM) at each plot 97

was defined as the mean of all trait values present in a given plot weighted by the relative 98

abundance of the species having each value (Garnier et al. 2004). The RDA variation partitioning 99

was performed according to Borcard et al. (1992), Borcard & Legendre (1994), and Peres-Neto et 100

al. (2006), and is schematically reported in Supplemental attachment 7. In this analysis, the 101

topographic variables were selected by the forward selection procedure of Blanchet et al. (2008), 102

whereas the species abundance data were chord transformed according to Legendre & Gallagher 103

(2001). Both the variation partitioning and permutations tests were made by the VEGAN package in 104

the R software (R Core Team 2012), while CWMs were calculated using the FD package. 105

Indicator species analysis. The two-step procedure of Indicator Species Analysis (ISA) 106

proposed by Ricotta et al. (2015) includes the species functional traits in order to evaluate the 107

diagnostic value of each species. During the first step (based-occurrence step), the species were 108

identified by the classic ISA based on the preferences of the species into the groups (Dufrêne & 109

Legendre 1997; De Cáceres & Legendre 2009). In the second step (based-trait step), the species 110

were selected among those considered in the first step to represent the groups from a functional 111

point of view. For the first step we used the phi coefficient (Chytrý et al. 2002) in the R 112

‘indicspecies’ package (De Cáceres & Legendre 2009). Species with phi 0.5 were considered 113

diagnostic (P significance 0.05, permutations = 999). The based-trait step of the second step was 114

run by using the R function FuncVal (P significance 0.05, permutations = 999), as reported by 115

Ricotta et al. (2015). 116

References 117

Allison LE. 1965. Organic carbon. In Black CA, Evans DD, Ensminger LE, White JL, Clarck FE 118

(eds.) Methods of Soil Analysis, Part 2. Agronomy Monograph, 9, American Society of 119

Agronomy, Madison, WI, pp. 1367–1378. 120

Blanchet FG, Legendre P, Borcard D. 2008. Forward selection of explanatory variables. Ecology 121

89: 2623–2632. 122

Borcard D, Legendre P. 1994. Environmental control and spatial structure in ecological. 123

communities: an example using oribatid mites (Acari, Oribatei). Environ Ecol Stat 1: 37–61. 124

Borcard D, Legendre P, Drapeau P. 1992. Partialling out the Spatial Component of Ecological 125

Variation. Ecology 73: 1045–1055. 126

Brookes PC, Landman A, Pruden G, Jenkinson DS. 1985. Chloroform fumigation and the release of 127

soil nitrogen: A rapid direct extraction method to measure microbial biomass nitrogen in soil. 128

Soil Biol Biochem 17: 837–842. 129

De Cáceres M, Legendre P. 2009. Associations between species and groups of sites: indices and 130

statistical inference. Ecology 90: 3566–74. 131

Chytrý M, Tichý L, Holt J, Botta-Dukát Z. 2002. Determination of diagnostic species with 132

statistical fidelity measures. J Veg Sci 13: 79–90. 133

Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner 134

J. 2015. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci Model Dev 8: 135

1991–2007. 136

Dufrêne M, Legendre P. 1997. Species assemblages and indicator species: the need for a flexible 137

asymmetrical approach. Ecol Monogr 67: 345–366. 138

Ellenberg H, Weber HE, Dull R, Wirth V, Werner W, Paulissen D. 1992. Zeigerwerte von Pflanzen 139

in Mitteleuropa. Scripta Geobot 18: 1–248. 140

Garnier E, Cortez J, Billès G, Navas ML, Roumet C, Debussche M, Laurent G, Blanchard A, Aubry 141

D, Bellmann A, Neill C, Toussaint JP. 2004. Plant functional markers capture ecosystem 142

properties during secondary succession. Ecology 85: 2630–2637. 143

Guisan A, Weiss SB, Weiss AD. 1999. GLM versus CCA spatial modeling of plant species 144

distribution. Plant Ecol 143: 107–122. 145

Koroleff F. 1983. Simultaneous oxidation of nitrogen and phosphorus compounds by persulfate. In 146

Grasshoff K, Eberhardt M, Kremling K (eds.) Methods of Seawater Analysis, Verlag Chemie, 147

Weinheimer, FRG, pp. 168–169. 148

Krzywinski A, Altman N. 2014. Points of significance: visualizing samples with box plots. Nat 149

Methods 11: 119–120. 150

Legendre P, Gallagher E. 2001. Ecologically meaningful transformations for ordination of species 151

data. Oecologia 129: 271–280. 152

Olsen SR, Cole C.V, Watanabe FS, Dean LA. 1954. Estimation of available phosphorus in soils by 153

extraction with sodium bicarbonate. US Government Printing Office, Washington, DC. 154

Peres-Neto PR, Legendre P, Dray S, Borcard D. 2006. Variation partitioning of species data 155

matrices: estimation and comparison of fractions. Ecology 87: 2614–25. 156

Pignatti S. 1982. Flora d’Italia. Edagricole, Firenze. 157

Pignatti S, Menegoni P, Pietrosanti S. 2005. Biondicazione attraverso le piante vascolari. Valori di 158

indicazione secondo Ellenberg (Zeigerwerte) per le specie della Flora d’Italia. Braun-159

Blanquetia 39: 1–97. 160

Qualls RG. 1989. Determination of total nitrogen and phosphorous in water using persulfate 161

oxidation: a modification for small sample volumes using the method of Koroleff (1983). The 162

biogeochemical properties of dissolved organic matter in a hardwood forest ecosystem: their 163

influence on the retention of nitrogen, phosphorus, and carbon. Appendix A pp. 131-138. 164

Ph.D. thesis, Institute of Ecology University of Georgia, USA. 165

R Core Team. 2012. R: A Language and Environment for Statistical Computing. 166

Ricotta C, Carboni M, Acosta ATR. 2015. Let the concept of indicator species be functional! J Veg 167

Sci 26: 839–847. 168

Vance ED, Brookes PC, Jenkinson DS. 1987. An extraction method for measuring soil microbial 169

biomass C. Soil Biol Biochem 19: 703–707. 170

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statistical inference. Journal of the Royal Statistical Society: Series A (Statistics in Society) 172

174: 247–295. 173

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Attachment 5. Biological form, chorological type, month the plants start flowering (M) and Ellenberg indicator values for the list of species found in the grasslands at the Sassotetto ski-resort (Sarnano, Italy).

L, light; T, temperature; C, continentality; U, soil moisture; R, soil reaction; N, soil nutrient availability.

Biological form Chorological type M L T C U R N Taxonomy

Hemicryptophyte scapose Eurosiberian M5 8 4 5 Achillea millefolium

Hemicryptophyte caespitose Circumboreal M6 7 4 3 3 Agrostis capillaris

Therophyte Sub-tropical M4 11 8 5 3 3 1 Aira caryophyllea

Hemicryptophyte reptant European – Caucasian M4 6 4 6 6 Ajuga reptans

Hemicryptophyte scapose Eurasian M6 9 3 5 4 2 2 Alchemilla glaucescens

Hemicryptophyte caespitose Arctic-alpine M7 5 5 3 Anthoxanthum odoratum subsp. nipponicum

Hemicryptophyte scapose Eurimediterranean M5 8 5 5 3 8 3 Anthyllis vulneraria subsp. weldeniana

Hemicryptophyte biannual European M4 7 5 5 4 8 Arabis hirsuta

Hemicryptophyte rosette Orophyte South-East European M5 7 7 5 3 7 2 Armeria canescens

Hemicryptophyte scapose Eurimediterranean M7 7 7 5 3 8 3 Asperula cynanchica

Hemicryptophyte caespitose Endemic M6 8 4 4 6 4 3 Avenula praetutiana

Hemicryptophyte caespitose Orophyte South-East European M7 8 3 5 4 4 3 Bellardiochloa variegata

Hemicryptophyte scapose Orophyte South-European M4 8 5 7 2 Biscutella laevigata

Hemicryptophyte caespitose Orophyte European M7 8 7 6 4 7 3 Brachypodium genuense

Hemicryptophyte caespitose Eurosiberian M5 6 4 2 Briza media

Hemicryptophyte caespitose Paleotemperate M5 8 5 7 3 8 3 Bromus erectus

Hemicryptophyte scapose Eurasian M6 7 7 4 7 Campanula glomerata

Hemicryptophyte scapose Endemic M7 7 5 5 5 2 Campanula micrantha

Hemicryptophyte scapose Eurasian M4 8 5 5 4 2 Carex caryophyllea

Hemicryptophyte caespitose Sub-endemic M5 6 5 5 3 6 2 Carex macrolepis

Hemicryptophyte rosette Central European M6 7 4 4 0 2 Carlina acaulis

Chamaephyte Sub-endemic M5 8 5 4 6 4 Cerastium arvense subsp. suffruticosum

Hemicryptophyte biannual Endemic M6 7 4 4 4 7 7 Cirsium morisianum

Geophyte Eurimediterranean M3 4 7 5 3 6 5 Crocus vernus

Hemicryptophyte scapose Eurasian M4 5 6 5 5 6 6 Cruciata glabra

Hemicryptophyte scapose Eurasian M4 7 6 5 5 5 5 Cruciata laevipes

Hemicryptophyte scapose European – Caucasian M6 8 4 4 4 5 4 Cyanus triumfetti

Hemicryptophyte caespitose European – Caucasian M4 8 5 4 5 5 4 Cynosurus cristatus

Geophyte European – Caucasian M4 8 7 4 4 6 5 Dactylorhiza sambucina

Hemicryptophyte caespitose European M5 8 5 4 6 3 2 Danthonia decumbens

Hemicryptophyte caespitose Sub-cosmopolitan M6 6 5 2 3 Deschampsia flexuosa

Hemicryptophyte scapose Orophyte South-European M5 6 7 5 4 2 5 Dianthus monspessulanus

Hemicryptophyte scapose Endemic M4 9 7 6 2 7 3 Erysimum pseudorhaeticum

Hemicryptophyte caespitose Eurimediterranean M5 11 6 5 1 6 2 Festuca circummediterranea

Hemicryptophyte caespitose Circumboreal M6 8 4 5 4 4 3 Festuca rubra

Hemicryptophyte scapose European Central M5 8 7 7 4 7 3 Filipendula vulgaris

Hemicryptophyte scapose Orophyte South-European M7 9 3 5 3 7 2 Galium anisophyllum

Hemicryptophyte scapose Stenomediterranean M5 11 8 4 2 6 2 Galium corrudifolium

Hemicryptophyte scapose European-Caucasian M6 7 6 6 4 7 3 Galium verum

Hemicryptophyte rosette South-East European M6 9 3 6 4 7 2 Gentiana dinarica

Hemicryptophyte scapose Orophyte South-European M6 8 4 5 4 4 2 Gentiana lutea

Hemicryptophyte rosette Eurasian M4 7 5 4 7 2 Gentiana verna

Hemicryptophyte biannual Endemic M6 7 3 4 3 7 2 Gentianella columnae

Chamaephyte North-East Mediterranean – Mountain M5 11 4 2 9 1 Globularia meridionalis

Geophyte Eurasian M5 8 4 5 4 7 3 Gymnadenia conopsea

Chamaephyte European – Caucasian M5 9 6 4 7 2

Helianthemum nummularium

subsp.obscurum

Chamaephyte European – Caucasian M5 9 7 4 2 7 2 Helianthemum oelandicum subsp. incanum

Hemicryptophyte scapose European M5 7 6 5 3 7 2 Hieracium cymosum

Hemicryptophyte rosette European – Caucasian M5 8 4 3 4 2 Hieracium pilosella

Hemicryptophyte caespitose Central-South European M5 9 5 2 7 2 Hippocrepis comosa

Hemicryptophyte scapose Paleotemperate M5 7 8 6 Hypericum perforatum

Hemicryptophyte scapose West Mediterranean – Mountain M6 7 4 4 4 2 Knautia purpurea

Hemicryptophyte caespitose Mediterranean – Mountain M5 11 7 6 3 7 1 Koeleria splendens

Hemicryptophyte scapose European M6 7 5 7 4 Laserpitium latifolium

Hemicryptophyte scapose Orophyte South-European M5 7 5 7 3 7 2 Laserpitium siler

Hemicryptophyte rosette Orophyte South-East European M5 9 6 5 3 7 2 Leontodon cichoraceus

Hemicryptophyte rosette European – Caucasian M6 8 4 4 3 Leontodon hispidus

Hemicryptophyte scapose Orophyte South-European M6 9 5 3 3 Leucanthemum adustum

Hemicryptophyte scapose Orophyte South-European M6 9 4 5 4 7 3 Linum alpinum

Therophyte Eurimediterranean – European M5 7 5 1 Linum catharticum

Hemicryptophyte scapose Paleotemperate M4 7 5 4 7 2 Lotus corniculatus

Hemicryptophyte caespitose European – Caucasian M4 7 4 4 4 3 2 Luzula campestris

Hemicryptophyte caespitose Orophyte South-European M6 3 3 4 5 2 3 Luzula sieberi

Geophyte Orophyte South-European M4 8 4 5 5 0 Narcissus poeticus

Hemicryptophyte caespitose South-European – South-Siberian M6 8 6 2 Nardus stricta

Hemicryptophyte rosette Orophyte South-West European M6 9 3 4 4 2 3 Pedicularis tuberosa

Hemicryptophyte scapose Orophyte South-European M6 8 3 5 8 2 Phyteuma orbiculare

Hemicryptophyte rosette South-European – South-Siberian M6 7 6 7 3 7 3 Plantago argentea

Hemicryptophyte caespitose Circumboreal M5 7 5 5 6 Poa alpina

Hemicryptophyte scapose Orophyte South-European M6 8 2 5 4 7 2 Polygala alpestris

Hemicryptophyte scapose South-European – South-Siberian M5 9 6 6 3 7 2 Polygala major

Hemicryptophyte scapose South-European – South-Siberian M4 9 7 7 3 7 3 Potentilla cinerea

Hemicryptophyte scapose Endemic M4 7 7 4 3 9 3 Potentilla rigoana

Hemicryptophyte rosette West-European M4 7 3 4 8 3 Primula veris

Hemicryptophyte scapose Endemic M5 8 3 4 3 7 1 Ranunculus apenninus

Hemicryptophyte scapose Orophyte South-European M5 6 6 5 6 Ranunculus breyninus

Hemicryptophyte scapose Endemic M7 11 3 4 3 7 1 Ranunculus pollinensis

Therophyte European Central M5 8 5 4 7 3 Rhinanthus alectorolophus

Therophyte Endemic M6 6 4 4 4 4 3 Rhinanthus personatus

Hemicryptophyte scapose North Mediterranean – Mountain M5 8 7 4 4 3 4 Rumex nebroides

Chamaephyte West and Central European M5 7 5 4 2 4 1 Sedum rupestre

Hemicryptophyte rosette Norh-East Mediterranean – Mountain M5 7 7 6 4 0 Senecio scopolii

Hemicryptophyte caespitose Endemic M4 10 4 4 2 7 4 Sesleria apennina

Hemicryptophyte caespitose Orophyte South-European M6 9 4 5 4 7 2 Silene ciliata

Hemicryptophyte scapose Eurimediterranean M6 7 6 5 3 8 4 Tanacetum corymbosum subsp. achilleae

Geophyte European Central-East M5 8 6 6 2 8 1 Thesium linophyllon

Chamaephyte Orophyte South-European M4 9 3 5 4 3 2 Thymus praecox subsp. polytrichus

Hemicryptophyte scapose Eurosiberian M5 7 5 4 4 7 5 Tragopogon pratensis

Hemicryptophyte scapose European – Caucasian M5 7 5 4 3 6 3 Trifolium alpestre

Hemicryptophyte scapose South-European – Pontic M5 7 6 3 8 2 Trifolium montanum

Hemicryptophyte caespitose South-European – South-Siberian M5 7 5 6 4 8 2 Trifolium ochroleucum

Hemicryptophyte scapose Eurosiberian M4 7 4 Trifolium pratense

Hemicryptophyte scapose South-East European M5 9 8 6 1 8 1 Trinia glauca

Hemicryptophyte scapose Eurimediterranean M5 11 4 5 5 7 3 Valeriana tuberosa

Hemicryptophyte caespitose Orophyte South-European M5 9 4 5 3 7 2 Veronica orsiniana

Hemicryptophyte scapose Endemic M3 11 4 3 2 7 1 Viola eugeniae

176

177

Attachment 6. Chorological types (modified from Pignatti 1982) and biological forms (Raunkiær 1934;

Pignatti 1982) considered in this study.

Chorology

Macrotypes Chorological types

Endemic Endemic, Sub-endemic

Mediterranean

Mediterranean-Mountain (including North, North-East, and West Mediterranean-

Mountain), Eurimediterranean, Stenomediterranean, Eurimediterranean-European

Eurasian

Eurasian, Paleotemperate, South-European – South-Siberian, European (including

Central, Central-East, Central-South, and South-East European), South-European –

Pontic, European – Caucasian

Atlantic West and central European, West-European

Orophytes

European Orophytes, South-European Orophytes, South-East European Orophytes,

South-West European Orophytes

Boreal Circumboreal, Eurosiberian, Arctic-alpine

Cosmopolitan Sub-cosmopolitan

Sub-tropical Sub-tropical

Biological forms Sub-forms

Therophytes

Geophytes

Hemicryptophytes caespitose, reptant, scapose, rosette, biannual

Chamaephytes

178

179

180

Attachment 7. Total variation partitioning scheme applied to the grasslands at the Sassotetto ski-181

resort (Sarnano, Italy). The variation of a response matrix (e.g., floristic composition and 182

community-weighted mean trait values (CWMs) of the grassland) is explained by the unique and 183

joint contribution of grassland management and topographic variables matrices. The total variation 184

is partitioned into fractions as follows: (1) fraction [a+b+c] based on all explanatory variables 185

(management + topography); (2) fraction [a+b] mostly based on the management variable; (3) 186

fraction [b+c] mostly based on the topographic variables; (4) the unique fraction of variation 187

explained by management [a] = [a+b+c] – [b+c]; (5) the unique fraction of variation explained by 188

topography [c] = [a+b+c] – [a+b]; (6) the common fraction of variation shared by management and 189

topography, [b] = [a+b+c] – [a] – [c]; (7) the residual fraction of total variation not explained by 190

either management or topography [d] = 1–[a+b+c] 191

192 193

194

195

196

Attachment 8. Mineralogical composition of the soils from the three studied areas at the Sassotetto

ski-resort (Sarnano, Italy). Numbers in parentheses are standard deviations (n=2).

Q P K C 2:1 M

%

Undisturbed grassland (UG)

A1 82(2) 2(1) 2(1) 0(-) 13(2) 1(0)

A2 84(3) 2(0) 1(0) 0(-) 12(3) 1(0)

A3 78(5) 8(2) 5(1) 0(-) 7(2) 2(0)

C/A 74(3) 12(1) 6(1) 0(-) 6(1) 2(0)

Ski-run with natural snow (NS)

A1 78(3) 4(1) 2(1) 0(-) 15(3) 1(0)

A2 78(2) 5(1) 1(0) 0(-) 14(3) 2(0)

A3 74(3) 10(1) 2(0) 0(-) 13(2) 1(0)

C/A 72(1) 11(2) 5(1) 1(0) 9(2) 2(0)

Ski-run with amassed and artificial snow (AS)

A1 77(2) 4(1) 2(0) 0(-) 16(3) 1(0)

A2 75(3) 5(1) 3(1) 0(-) 16(3) 1(0)

A3 74(1) 8(2) 2(0) 0(-) 15(2) 1(1)

C/A 73(2) 11(2) 5(1) 0(-) 10(3) 1(0)

Q=quartz, P=plagioclases, K=kaolinite, C=calcite, 2:1=clay minerals with 2:1 structure, M=micas. 197

198

199

Attachment 9. Mean dissolved organic C (DOC), dissolved organic N (DON),

microbial biomass C (MB-C), microbial biomass N (MB-C), NH4-N, and NO3-N

contents of the soils from the three studied areas at the Sassotetto ski-resort (Sarnano,

Italy). Numbers in parentheses are standard deviations (n=2).

DOC DON MB-C MB-N NH4-N NO3-N

mg kg-1

Undisturbed grassland (UG)

A1 275(35) 64(16) 222(78) 33(12) 13(5) 1(1)

A2 258(37) 56(12) 157(47) 26(10) 11(5) 1(1)

A3 168(24) 40(8) 69(26) 14(4) 9(4) 1(0)

C/A 124(19) 23(4) 55(28) 9(3) 6(3) 1(0)

Ski-run with natural snow (NS)

A1 319(33) 76(14) 229(67) 26(11) 8(3) 1(1)

A2 236(30) 59(10) 138(39) 19(7) 7(3) 1(1)

A3 241(28) 52(8) 64(21) 11(3) 6(2) 1(0)

C/A 185(24) 36(9) 32(19) 5(3) 2(2)

Attachment 10. Boxplot of soil and temperature data for the Sassotetto ski-resort (Sarnano, Italy). 203

Values are standardized to cover the range 0-1. 204

Legend. 205

UG, undisturbed grassland area; NS, ski-runs with natural snow; AS, ski-runs with amassed and 206

artificial snow. 207

WT, mean winter soil temperature; ST, mean summer soil temperature; AT, mean annual soil 208

temperature; avP, available phosphorous; MB-C, soil microbial biomass carbon; MB-N, soil 209

microbial biomass nitrogen; TOC, total organic carbon; DOC, dissolved organic carbon; DON, 210

dissolved organic nitrogen; HC, humic carbon. 211

212

Attachment 11. RDA triplot of the chord transformed data of grassland vegetation abundance of 213

Sassotetto ski-resort (Sarnano, Italy) explained uniquely by management (fraction [a], see 214

Supplemental attachment 7 and Table IV), once topographic variables have been excluded. 215

216

Legend. 217

UG, undisturbed grassland area; NS, ski-runs with natural snow; AS, ski-runs with amassed and 218

artificial snow. Anthnipp, Anthoxanthum odoratum nipponicum; Bracgenu, Brachypodium 219

genuense; Brizmedi, Briza media; Bromerec, Bromus erectus; Campmicr, Campanula micrantha; 220

Caremacr, Carex macrolepis; Carlacau, Carlina acaulis; Descflex, Deschapsia flexuosa; Festrubr, 221

Festuca rubra; Filivulg, Filipendula vulgaris; Galicorr, Galium corrudifolium; Galiveru, Galium 222

verum; Gentlute, Gentiana lutea; Knaupurp, Knautia purpurea; Leonhisp, Leontodon hispidus; 223

Leucadus, Leucanthemum adustum; Phytorbi, Phyteuma orbiculare; Ranuapen, Ranunculus 224

apenninus; Rhinalec, Rhinanthus alectorolophus; Rhinpers, Rhinanthus personatus; Trifalpe, 225

Trifolium alpestre; Veroorsi, Veronica orsiniana. 226

227

228

229

Attachment 12. RDA triplot of the grassland community-weighted mean trait values (CWMs) of 230

Sassotetto ski-resort (Sarnano, Italy) explained uniquely by management (fraction [a], see 231

Supplemental attachment 7 and Table IV), once topographic variables have been excluded. 232

233

Legend. 234

UG, undisturbed grassland area; NS, ski-runs with natural snow; AS, ski-runs with amassed and 235

artificial snow. 236

Ellenberg indicators (according to Pignatti 2005): R, soil reaction; N, nutrient availability; C, 237

continentality; U, soil moisture; T, temperature; L, light. 238

Biological forms: G, Geophytes; CH, Chamaephytes; HS, Hemicryptophytes scapose; TB, 239

Therophytes; HR, Hemicryptophytes rosette; HC, Hemicryptophytes caespitose. 240

Chorological types: End, Endemic; Eur, Eurasian; Med, Mediterranean; Bor, Boreal; Oro, 241

Orophytes. 242

The month the plants start flowering: M4, April; M5, May; M6, June; M7, July. 243

244

245